1. Field of the Invention
The present invention relates to an image retrieval method, and more particularly, to an image retrieval method based on color and texture describers that are significant in human visual perception. The present application is based on Korean Application No. 2001-1130, filed on Jan. 9, 2001, which is incorporated herein by reference.
2. Description of the Related Art
In recent years, a sudden increase in multimedia data has facilitated research in the fields of data retrieval and databases for efficient multimedia data management. In particular, research in the field of image retrieval has focused on an alternative to existing text-based image retrieval methods or further advanced image retrieval methods. For text-based data retrieval, a great effort needs to be made to retrieve the desired data from a large-scale database, and it is unsuitable for partial-feature-based retrieval or quantitative data retrieval. To address these drawbacks, approaches have been suggested for a retrieval method using numeric feature descriptors which well represent the features of the multimedia data.
Color and texture features, which are dominant characteristics in human visual perception, can be expressed by feature descriptors. According to a conventional image retrieval method, an image database in which data images are indexed by a feature descriptor is built up, and desired images are retrieved from the image database based on the feature descriptor. An example of a conventional color feature based image retrieval method includes extracting color vectors, quantizing the color vectors, getting a representative color vector from the quantized color vectors, and retrieving the image data using the representative color vector. A conventional texture feature based image retrieval method uses the co-occurrence matrix, the Gaussian Markov Random Field (GMRF) models, etc. A recent method of texture feature extraction uses a Gabor filter that enables a multi-channel approach suitable for the human perception mechanism of visual images.
In the conventional image retrieval methods, color and texture based data retrievals are separately carried out, and color and texture features are extracted from the entire image. For this reason, the conventional methods cannot be applied to an image having a plurality of objects therein or expressed in multiple colors and textures. Either the color or the texture features are available for retrieving particular image data. Thus, there is a problem of incorrect image retrieval results.
To solve these problems, another conventional image retrieval method retrieves a target image in the vector space formed for both color and texture vector components. Here, the vector space is formed through simple summations of the color and texture vector components, thereby increasing dimensionality of the vector space. Thus, there is a need to redefine a distance function to the vector space with the increased dimensionality and to calculate a weighting factor for each component, which is relatively burdensome. The computations are highly complex and the results may be incorrect.